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# origin: https://github.com/intel/openvino-ai-plugins-gimp/blob/ae93e7291fab6d372c958da18e497acb9d927055/gimpopenvino/tools/openvino_common/models_ov/stable_diffusion_engine.py#L748
import os
from typing import Union, Optional, Any, List, Dict
import torch
from openvino.runtime import Core
from diffusers import DiffusionPipeline, LCMScheduler, ImagePipelineOutput
from diffusers.image_processor import VaeImageProcessor
from transformers import CLIPTokenizer
class LatentConsistencyEngine(DiffusionPipeline):
def __init__(
self,
model="SimianLuo/LCM_Dreamshaper_v7",
tokenizer="openai/clip-vit-large-patch14",
device=["CPU", "CPU", "CPU"],
):
super().__init__()
try:
self.tokenizer = CLIPTokenizer.from_pretrained(model, local_files_only=True)
except:
self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer)
self.tokenizer.save_pretrained(model)
self.core = Core()
self.core.set_property({'CACHE_DIR': os.path.join(model, 'cache')}) # adding caching to reduce init time
# text features
print("Text Device:", device[0])
self.text_encoder = self.core.compile_model(os.path.join(model, "text_encoder.xml"), device[0])
self._text_encoder_output = self.text_encoder.output(0)
# diffusion
print("unet Device:", device[1])
self.unet = self.core.compile_model(os.path.join(model, "unet.xml"), device[1])
self._unet_output = self.unet.output(0)
self.infer_request = self.unet.create_infer_request()
# decoder
print("Vae Device:", device[2])
self.vae_decoder = self.core.compile_model(os.path.join(model, "vae_decoder.xml"), device[2])
self.infer_request_vae = self.vae_decoder.create_infer_request()
self.safety_checker = None #pipe.safety_checker
self.feature_extractor = None #pipe.feature_extractor
self.vae_scale_factor = 2 ** 3
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.scheduler = LCMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear"
)
def _encode_prompt(
self,
prompt,
num_images_per_prompt,
prompt_embeds: None,
):
r"""
Encodes the prompt into text encoder hidden states.
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
num_images_per_prompt (`int`):
number of images that should be generated per prompt
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
"""
if prompt_embeds is None:
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(
prompt, padding="longest", return_tensors="pt"
).input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[
-1
] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
)
prompt_embeds = self.text_encoder(text_input_ids, share_inputs=True, share_outputs=True)
prompt_embeds = torch.from_numpy(prompt_embeds[0])
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(
bs_embed * num_images_per_prompt, seq_len, -1
)
# Don't need to get uncond prompt embedding because of LCM Guided Distillation
return prompt_embeds
def run_safety_checker(self, image, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(
image, output_type="pil"
)
else:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(
feature_extractor_input, return_tensors="pt"
)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept
def prepare_latents(
self, batch_size, num_channels_latents, height, width, dtype, latents=None
):
shape = (
batch_size,
num_channels_latents,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if latents is None:
latents = torch.randn(shape, dtype=dtype)
# scale the initial noise by the standard deviation required by the scheduler
return latents
def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32):
"""
see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps: torch.Tensor: generate embedding vectors at these timesteps
embedding_dim: int: dimension of the embeddings to generate
dtype: data type of the generated embeddings
Returns:
embedding vectors with shape `(len(timesteps), embedding_dim)`
"""
assert len(w.shape) == 1
w = w * 1000.0
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
emb = w.to(dtype)[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero pad
emb = torch.nn.functional.pad(emb, (0, 1))
assert emb.shape == (w.shape[0], embedding_dim)
return emb
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = 512,
width: Optional[int] = 512,
guidance_scale: float = 7.5,
scheduler = None,
num_images_per_prompt: Optional[int] = 1,
latents: Optional[torch.FloatTensor] = None,
num_inference_steps: int = 4,
lcm_origin_steps: int = 50,
prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
model: Optional[Dict[str, any]] = None,
seed: Optional[int] = 1234567,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
callback = None,
callback_userdata = None
):
# 1. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if seed is not None:
torch.manual_seed(seed)
#print("After Step 1: batch size is ", batch_size)
# do_classifier_free_guidance = guidance_scale > 0.0
# In LCM Implementation: cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond) , (cfg_scale > 0.0 using CFG)
# 2. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
num_images_per_prompt,
prompt_embeds=prompt_embeds,
)
#print("After Step 2: prompt embeds is ", prompt_embeds)
#print("After Step 2: scheduler is ", scheduler )
# 3. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, original_inference_steps=lcm_origin_steps)
timesteps = self.scheduler.timesteps
#print("After Step 3: timesteps is ", timesteps)
# 4. Prepare latent variable
num_channels_latents = 4
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
latents,
)
latents = latents * self.scheduler.init_noise_sigma
#print("After Step 4: ")
bs = batch_size * num_images_per_prompt
# 5. Get Guidance Scale Embedding
w = torch.tensor(guidance_scale).repeat(bs)
w_embedding = self.get_w_embedding(w, embedding_dim=256)
#print("After Step 5: ")
# 6. LCM MultiStep Sampling Loop:
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
if callback:
callback(i+1, callback_userdata)
ts = torch.full((bs,), t, dtype=torch.long)
# model prediction (v-prediction, eps, x)
model_pred = self.unet([latents, ts, prompt_embeds, w_embedding],share_inputs=True, share_outputs=True)[0]
# compute the previous noisy sample x_t -> x_t-1
latents, denoised = self.scheduler.step(
torch.from_numpy(model_pred), t, latents, return_dict=False
)
progress_bar.update()
#print("After Step 6: ")
#vae_start = time.time()
if not output_type == "latent":
image = torch.from_numpy(self.vae_decoder(denoised / 0.18215, share_inputs=True, share_outputs=True)[0])
else:
image = denoised
#print("vae decoder done", time.time() - vae_start)
#post_start = time.time()
#if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
#else:
# do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
#print ("After do_denormalize: image is ", image)
image = self.image_processor.postprocess(
image, output_type=output_type, do_denormalize=do_denormalize
)
return ImagePipelineOutput([image[0]]) |